Data is the raw ingredient of machine learning. Processing data can produce information; for example, measuring the height of a portion of a school's students (data) and calculating their average (processing) can give us an idea of the whole school's height (information). If we process the data further, for example, by grouping males and females and calculating two averages – one for each group, we will gain more information, as we will have an idea about the average height of the school's males and females. Machine learning strives to produce the most information possible from any given data. In this example, we produced a very basic predictive model. By calculating the two averages, we can predict the average height of any student just by knowing whether the student is male or female.
The set of data that a machine learning algorithm...
